{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/supervised-topic-models","title":"Supervised Topic Models","arxiv_id":"1003.0783","date":"2010-03-03","proceeding":"NeurIPS 2007 12","authors":["David M. Blei","Jon D. McAuliffe"],"abstract":"We introduce supervised latent Dirichlet allocation (sLDA), a statistical\nmodel of labelled documents. The model accommodates a variety of response\ntypes. We derive an approximate maximum-likelihood procedure for parameter\nestimation, which relies on variational methods to handle intractable posterior\nexpectations. Prediction problems motivate this research: we use the fitted\nmodel to predict response values for new documents. We test sLDA on two\nreal-world problems: movie ratings predicted from reviews, and the political\ntone of amendments in the U.S. Senate based on the amendment text. We\nillustrate the benefits of sLDA versus modern regularized regression, as well\nas versus an unsupervised LDA analysis followed by a separate regression.","url_abs":"http://arxiv.org/abs/1003.0783v1","url_pdf":"http://arxiv.org/pdf/1003.0783v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"supervised-topic-models","repo_url":"https://github.com/labixiaoK/lda","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1003.0783","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}